Affiliation:
1. Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
2. School of Software Convergence, Myongji University, Seoul 03674, Republic of Korea
Abstract
Understanding condition-specific biological mechanisms from RNA-seq data requires comprehensive analysis of gene expression data, from the gene to the network level. However, this requires computational expertise, which limits the accessibility of data analysis for understanding biological mechanisms. Therefore, the development of an easy-to-use and comprehensive analysis system is essential. In response to this issue, we present TFNetPropX, a user-friendly web-based platform designed to perform gene-level, gene-set-level, and network-level analysis of RNA-seq data under two different conditions. TFNetPropX performs comprehensive analysis, from DEG analysis to network propagation, to predict TF-affected genes with a single request, and provides users with an interactive web-based visualization of the results. To demonstrate the utility of our system, we performed analysis on two TF knockout RNA-seq datasets and effectively reproduced biologically significant findings. We believe that our system will make it easier for biological researchers to gain insights from different perspectives, allowing them to develop diverse hypotheses and analyses.
Funder
2022 Research Fund of Myongji University
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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